Impact of a Best Practices Program in Patients with Relapsed/Refractory Multiple Myeloma Receiving Selinexor

Best practice (BP) in cancer care consists of a multifaceted approach comprising individualized treatment plans, evidence-based medicine, the optimal use of supportive care and patient education. We investigated the impact of a BP program in patients with relapsed/refractory multiple myeloma (RRMM) receiving selinexor. Features of the BP program that were specific to selinexor were initiating selinexor at doses ≤80 mg once weekly and the upfront use of standardized antiemetics. Study endpoints included time to treatment failure (TTF), duration of therapy, dose limiting toxicities and overall survival. Comparative analysis on TTF and duration of therapy was conducted using a log-rank test and multivariate Cox proportional hazard regression. Over the ensuing 12-month post-BP period, 41 patients received selinexor-based therapy compared to 68 patients who received selinexor-based therapy pre-BP implementation. Patients treated in the post-BP period had reductions in TTF (hazard ratio [HR] = 0.50; 95% CI: 0.27 to 0.92). Patients in the pre-BP period were four times more likely to stop therapy than those in the post-period (odds ratio [OR] = 4.0, 95% CI: 1.75 to 9.3). The findings suggest a BP program tailored to selinexor could increase the time to treatment failure, increase treatment duration and lower the incidence of drug limiting toxicities.

Real-World Outcomes of Trilaciclib Among Patients with Extensive-Stage Small Cell Lung Cancer Receiving Chemotherapy

INTRODUCTION

Trilaciclib was recently approved in the USA for reducing chemotherapy-induced myelosuppression (CIM) among adults with extensive-stage small cell lung cancer (ES-SCLC) when administered prior to chemotherapy. There is limited understanding of real-world outcomes of trilaciclib.

METHODS

A comprehensive literature review was conducted using a keyword search in the MEDLINE, Embase, and conference abstracts. Additional studies were identified through communications with the authors of relevant studies. Published and unpublished real-world studies of trilaciclib- and comparable non-trilaciclib-treated patients with ES-SCLC were included. Evidence on myelosuppressive hematologic adverse events (HAEs), cytopenia-related healthcare utilization, and other reported outcomes (e.g., hospitalizations, dose reduction, and treatment delay) were synthesized. If feasible, outcomes were compared qualitatively between the trilaciclib and historical reference groups, and between first-line trilaciclib initiators and the overall trilaciclib population. Weighted averages were estimated for selected outcomes using sample size as the weight.

RESULTS

The literature search identified five unique studies based on eight records-two included trilaciclib only, two non-trilaciclib only, and one both. In trilaciclib cohorts, the weighted average prevalence of grade ≥ 3 myelosuppressive HAEs in ≥ 1 lineage, ≥ 2 lineages, and all three lineages was 40.5%, 14.5%, and 7.5%, respectively. All rates were numerically lower compared to the historical non-trilaciclib cohorts (58.8%, 28.0%, 13.0% respectively). Cytopenia-related healthcare utilization was also lower in the trilaciclib cohorts. In general, first-line trilaciclib initiators had numerically lower myelosuppressive HAEs and cytopenia-related healthcare utilization than the overall trilaciclib patients.

CONCLUSIONS

The existing evidence suggests that trilaciclib may reduce single and multilineage grade ≥ 3 myelosuppressive HAEs and cytopenia-related healthcare utilization among patients with ES-SCLC in the real world. It is a promising new treatment for CIM prevention in ES-SCLC and may bring greater benefits to first-line trilaciclib initiators. Future studies are recommended to further evaluate the real-world effectiveness of trilaciclib.

Burden of chemotherapy-induced myelosuppression among patients with extensive-stage small cell lung cancer: A retrospective study from community oncology practices

BACKGROUND

Myelosuppression is a major dose-limiting complication of chemotherapy for patients with extensive-stage small cell lung cancer (ES-SCLC). The objective was to describe the burden of myelosuppression, treatment patterns, and supportive care use among patients with ES-SCLC treated with chemotherapy in a US community oncology setting.

METHODS

This retrospective cohort study used structured electronic medical record (EMR) data from the Florida Cancer Specialists & Research Institute between January 2013 and December 2020. Adult patients with ES-SCLC who were treated with chemotherapy between September 2013 and November 2020 were identified. The index date was the date of the first chemotherapy-containing line of therapy (LOT). Patients were followed for a minimum of 30 days after index (unless patient died) until December 31, 2020, or end of activity in the EMR data, whichever occurred first. Incidence and frequency of myelosuppressive episodes/events, treatment patterns, eligibility for red blood cell (RBC) or platelet transfusions, and supportive care use (granulocyte colony-stimulating factor [G-CSF], erythropoiesis-stimulating agents [ESAs], intravenous [IV] hydration) during the follow-up period were reported.

RESULTS

The study population included 1239 patients. Most (94.0%) patients started first-line chemotherapy at index. During follow-up and across all chemotherapy-containing LOTs, 1222 (98.6%) patients had at least 1 myelosuppressive episode; 62.1% of patients had grade ≥ 3 myelosuppressive episodes in at least one lineage, 33.9% had grade ≥ 3 myelosuppressive episodes in at least two lineages, and 15.5% had grade ≥ 3 myelosuppressive episodes in all three lineages. Supportive care use included 89.7% of patients who received G-CSF, 24.4% who received ESAs, and 52.1% who received IV volume expansion. Almost one-third (32.6%) of patients were eligible to receive RBC transfusions based on lab values (hemoglobin < 8 g/dL).

CONCLUSION

There is a high burden related to multilineage myelosuppression among chemotherapy-treated patients with ES-SCLC in the community oncology setting. Reducing myelosuppression could make chemotherapy treatment safer, reduce the need for supportive care, and potentially prevent the treatment of complications.

Evaluation of freely available data profiling tools for health data research application: a functional evaluation review

OBJECTIVES

To objectively evaluate freely available data profiling software tools using healthcare data.

DESIGN

Data profiling tools were evaluated for their capabilities using publicly available information and data sheets. From initial assessment, several underwent further detailed evaluation for application on healthcare data using a synthetic dataset of 1000 patients and associated data using a common health data model, and tools scored based on their functionality with this dataset.

SETTING

Improving the quality of healthcare data for research use is a priority. Profiling tools can assist by evaluating datasets across a range of quality dimensions. Several freely available software packages with profiling capabilities are available but healthcare organisations often have limited data engineering capability and expertise.

PARTICIPANTS

28 profiling tools, 8 undergoing evaluation on synthetic dataset of 1000 patients.

RESULTS

Of 28 potential profiling tools initially identified, 8 showed high potential for applicability with healthcare datasets based on available documentation, of which two performed consistently well for these purposes across multiple tasks including determination of completeness, consistency, uniqueness, validity, accuracy and provision of distribution metrics.

CONCLUSIONS

Numerous freely available profiling tools are serviceable for potential use with health datasets, of which at least two demonstrated high performance across a range of technical data quality dimensions based on testing with synthetic health dataset and common data model. The appropriate tool choice depends on factors including underlying organisational infrastructure, level of data engineering and coding expertise, but there are freely available tools helping profile health datasets for research use and inform curation activity.

The impact of COVID-19 pandemic on access to new patient cancer care as evaluated in the Integra Connect Database (ICD)

e18792

Background: The Covid-19 pandemic has affected all aspects of life. Integra Connect sought to assess its impact on new visits for cancer care by querying its Electronic Medical Record and Claims database as a surrogate for understanding Covid’s impact on cancer care in the larger United States population. Methods: Using Real World Data (RWD) from over 1200 oncology providers in 14 large practice settings, comprising 250 plus care sites in the ICD, we measured new patient (Pt) and established Pt visits from 2018 through 2020. Centers for Medicare & Medicaid Services (CMS) codes for new and established Pt’s were applied to define cohorts. Disease states were defined using CMS Oncology Care Model (OCM) mapping to diagnosis codes. Because the ICD is heavily based in the Eastern US, we conducted a geographic analysis by segmenting providers by Northeast (NE) with 506 providers from NY, NJ, PA, and VA and Southeast (SE) with 578 providers from FL, GA, SC, and AL. We looked at visits across all tumor types and identified breast cancer (BC) and colorectal cancer (CC) as likely to be most affected by decreased screening, and multiple myeloma (MM) and advanced prostate cancer (APC) as likely to be vulnerable to delay in initiation of first treatment since treatment often trails diagnosis. Results: We found a decline in new patient volume (NPV) in ‘20 of -1% compared to ‘19; this compared to an anticipated increase of +7% based on growth in NPV in the ICD from ‘18 to ‘19. In the NE we saw NPV decrease in ‘20 by -7% vs. ‘19 but increase by +6% in the SE compared to ‘19. In comparing NPV ‘20 to ’19 and ’19 to ‘18, we saw a smaller increase for BC of +4% vs. +6% and for CC of +5% vs. +7%, respectively. Whereas in MM it was -7% vs. +3% and APC -6% vs. +8%. (See Table). Conclusions: Covid-19 has negatively impacted cancer care access. This RWD shows the number of both newly diagnosed Pts and those with initial oncologic intervention in established Pts, where symptoms traditionally have determined initiation of treatment, has declined year-over-year. The American Cancer Society projected new Pt cases to increase +2% in ‘20 to 1.806 million ( ACS, Cancer Facts and Figures 2020), whereas in the ICD, that figure was projected +7% but resulted in -1%. This suggests a major expected reduction of new Pt cases in the US at large. The drop in cases during Covid-19 in our data was greater in the NE compared to the SE. In addition, the drop in the NE in our data was earlier and more prolonged than SE. While recovery occurred in both regions, after an initial overshoot following lockdowns, volumes plateaued at levels lower than pre-pandemic.[Table: see text]

Network graph representation of COVID-19 scientific publications to aid knowledge discovery

Introduction

Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult.

Methods

A graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network.

Results

The network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool.

Conclusion

Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.

Unsupervised 3D Ring Template Searching as an Ideas Generator for Scaffold Hopping: Use of the LAMDA, RigFit, and Field-Based Similarity Search (FBSS) Methods

Crystal structures taken from the Cambridge Structural Database were used to build a ring scaffold database containing 19 050 3D structures, with each such scaffold then being used to generate a centroid connecting path (CCP) representation. The CCP is a novel object that connects ring centroids, ring linker atoms, and other important points on the connection path between ring centroids. Unsupervised searching in the scaffold and CCP data sets was carried out using the atom-based LAMDA and RigFit search methods and the field-based similarity search method. The performance of these methods was tested with three different ring scaffold queries. These searches demonstrated that unsupervised 3D scaffold searching methods can find not only the types of ring systems that might be retrieved in carefully defined pharmacophore searches (supervised approach) but also additional, structurally diverse ring systems that could form the starting point for lead discovery programs or other scaffold-hopping applications. Not only are the methods effective but some are sufficiently rapid to permit scaffold searching in large chemical databases on a routine basis.

OptiDock: Virtual HTS of Combinatorial Libraries by Efficient Sampling of Binding Modes in Product Space

Products from combinatorial libraries generally share a common core structure that can be exploited to improve the efficiency of virtual high-throughput screening (vHTS). In general, it is more efficient to find a method that scales with the total number of reagents (Sigma growth) rather with the number of products (Pi growth). The OptiDock methodology described herein entails selecting a diverse but representative subset of compounds that span the structural space encompassed by the full library. These compounds are docked individually using the FlexX program (Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. J. Mol. Biol. 1995, 251, 470-489) to define distinct docking modes in terms of reference placements for combinatorial core atoms. Thereafter, substituents in R-cores (consisting of the core structure substituted at a single variation site) are docked, keeping the core atoms fixed at the coordinates dictated by each reference placement. Interaction energies are calculated for each docked R-core with respect to the target protein, and energies for whole compounds are calculated by finding the reference core placement for which the sum of corresponding R-core energies is most negative. The use of diverse whole compounds to define binding modes is a key advantage of the protocol over other combinatorial docking programs. As a result, OptiDock returns better-scoring conformers than does serially applied FlexX. OptiDock is also better able to find a viable docked pose for each library member than are other combinatorial approaches.

Evaluation of a novel infrared range vibration-based descriptor (EVA) for QSAR studies. 1. General application

A novel molecular descriptor (EVA) based upon calculated infrared range vibrational frequencies is evaluated for use in QSAR studies. The descriptor is invariant to both translation and rotation of the structures concerned. The method was applied to 11 QSAR datasets exhibiting both a range of biological endpoints and various degrees of structural diversity. This study demonstrates that robust QSAR models can be obtained using the EVA descriptor and examines the effect of EVA parameter changes on these models; recommendations are made as to the appropriate choice of parameters. The performance of EVA was found to be comparable in statistical terms to that of CoMFA, despite the fact that EVA does not require the generation of a structural alignment. Models derived using semiempirical (MOPAC AM1 and PM3) and AMBER mechanics calculated normal mode frequencies are compared, with the overall conclusion that the semiempirical methods perform equally well and both outperform the AMBER-based models.

EVA: a new theoretically based molecular descriptor for use in QSAR/QSPR analysis

A new descriptor of molecular structure, EVA, for use in the derivation of robustly predictive QSAR relationships is described. It is based on theoretically derived normal coordinate frequencies, and has been used extensively and successfully in proprietary chemical discovery programmes within Shell Research. As a result of informal dissemination of the methodology, it is now being used successfully in related areas such as pharmaceutical drug discovery. Much of the experimental data used in development remain proprietary, and are not available for publication. This paper describes the method and illustrates its application to the calculation of nonproprietary data, log P(ow), in both explanatory and predictive modes. It will be followed by other publications illustrating its application to a range of data derived from biological systems.